Defining expert specialization in high-dimensional continuous modalities

Determine a general framework for defining and assigning expert specialization in sparse Mixture-of-Experts architectures for high-dimensional, continuous modalities in multimodal generative models. Specifically, ascertain whether human motion experts should specialize by anatomical parts (for example, limbs) versus motion styles, and whether image experts should specialize by spatial regions, object types, or levels of visual abstraction, in order to support modularity across modalities.

Background

The survey argues that as multimodal systems adopt Mixture-of-Experts architectures, deciding how to partition expertise becomes critical for scalability, interpretability, and controllability. Unlike text, continuous modalities such as images and human motion introduce spatial and temporal structure that complicates how experts should be defined and routed.

While prior works (for example, RAPHAEL and related spatial or part-specific approaches) explore localized specialization, there is no unified methodology for determining expert granularity or scope across different modalities. Establishing principled criteria for expert design is essential to build modular, efficient, and generalizable multimodal systems.